Language models are few-shot learners

FSA-GRPO: Teaching Auditory LLMs to Use Few-shot Demonstrations

arXiv:2606. 02615v1 Announce Type: cross Abstract: Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognition.

Efficient training of language models to fill in the middle

Neuron-Aware Active Few-Shot Learning for LLMs

arXiv:2607. 02423v1 Announce Type: cross Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high performance.

Shared Doubt: Zero-Shot Cross-Lingual Confidence Estimation for Language Models

arXiv:2605. 31220v2 Announce Type: replace-cross Abstract: Confidence estimation (CE), i.

Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation

arXiv:2606. 30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.

ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models

arXiv:2605. 18879v3 Announce Type: replace-cross Abstract: Large language models inevitably retain sensitive information, defined as inputs that may induce harmful generations, due to training on massive web corpora, raising concerns for privacy and safety.